forked from phoenix-oss/llama-stack-mirror
c9e5578151
See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. This PR finishes off all the stragglers and migrates everything to the new naming.
135 lines
3.8 KiB
Python
135 lines
3.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import os
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import pytest
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import pytest_asyncio
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from llama_stack.apis.models import ModelInput, ModelType
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from llama_stack.apis.tools import ToolGroupInput
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from llama_stack.distribution.datatypes import Api, Provider
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from llama_stack.providers.tests.resolver import construct_stack_for_test
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from ..conftest import ProviderFixture
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@pytest.fixture(scope="session")
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def tool_runtime_memory_and_search() -> ProviderFixture:
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="memory-runtime",
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provider_type="inline::memory-runtime",
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config={},
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),
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Provider(
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provider_id="tavily-search",
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provider_type="remote::tavily-search",
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config={
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"api_key": os.environ["TAVILY_SEARCH_API_KEY"],
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},
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),
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Provider(
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provider_id="wolfram-alpha",
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provider_type="remote::wolfram-alpha",
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config={
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"api_key": os.environ["WOLFRAM_ALPHA_API_KEY"],
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},
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),
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],
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)
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@pytest.fixture(scope="session")
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def tool_group_input_memory() -> ToolGroupInput:
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return ToolGroupInput(
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toolgroup_id="builtin::memory",
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provider_id="memory-runtime",
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)
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@pytest.fixture(scope="session")
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def tool_group_input_tavily_search() -> ToolGroupInput:
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return ToolGroupInput(
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toolgroup_id="builtin::web_search",
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provider_id="tavily-search",
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)
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@pytest.fixture(scope="session")
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def tool_group_input_wolfram_alpha() -> ToolGroupInput:
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return ToolGroupInput(
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toolgroup_id="builtin::wolfram_alpha",
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provider_id="wolfram-alpha",
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)
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TOOL_RUNTIME_FIXTURES = ["memory_and_search"]
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@pytest_asyncio.fixture(scope="session")
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async def tools_stack(
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request,
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inference_model,
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tool_group_input_memory,
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tool_group_input_tavily_search,
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tool_group_input_wolfram_alpha,
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):
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fixture_dict = request.param
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providers = {}
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provider_data = {}
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for key in ["inference", "vector_io", "tool_runtime"]:
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fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
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providers[key] = fixture.providers
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if key == "inference":
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providers[key].append(
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Provider(
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provider_id="tools_memory_provider",
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provider_type="inline::sentence-transformers",
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config={},
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)
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)
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if fixture.provider_data:
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provider_data.update(fixture.provider_data)
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inference_models = (
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inference_model if isinstance(inference_model, list) else [inference_model]
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)
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models = [
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ModelInput(
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model_id=model,
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model_type=ModelType.llm,
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provider_id=providers["inference"][0].provider_id,
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)
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for model in inference_models
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]
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models.append(
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ModelInput(
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model_id="all-MiniLM-L6-v2",
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model_type=ModelType.embedding,
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provider_id="tools_memory_provider",
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metadata={"embedding_dimension": 384},
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)
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)
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test_stack = await construct_stack_for_test(
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[
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Api.tool_groups,
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Api.inference,
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Api.vector_io,
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Api.tool_runtime,
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],
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providers,
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provider_data,
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models=models,
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tool_groups=[
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tool_group_input_tavily_search,
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tool_group_input_wolfram_alpha,
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tool_group_input_memory,
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],
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)
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return test_stack
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